In this section we evaluate the models' performance in the setting of MBRL, where both the model and the policy are updated iteratively [1]}, [2]}, [3]}, [4]}, [5]}. Importantly, observation update during execution of a trajectory happens only every fifth step in the trajectory. Figure () shows that GEM achieves higher rewards in all the environment with the same amount of data.
